![]() A subject matter expert identifies the terms relevant to the taxonomies. An automatic term extractor creates a list of terms indicative of the documents' subject matter. Automatically constructed indexes help identify when the structured record is an appropriate response to a query. Documents are transformed from clear text into a structured record. Documents (or other knowledge containers) in an organization and retrieval subsystem may be manually or automatically classified into taxonomies. Some of the problems that the article points out might well be resolved if the evaluators were to publish a detailed description of their procedures and the rationale that led to their adoption, but other problems would clearly remain./par>Ībstract: A method and system organize and retrieve information using taxonomies, a document classifier, and an autocontextualizer. The purpose of this article is to attempt to identify the shortcomings of the Lincoln Lab effort in the hope that future efforts of this kind will be placed on a sounder footing. The appropriateness of the evaluation techniques used needs further investigation. One problem is that the evaluators have published relatively little concerning some of the more critical aspects of their work, such as validation of their test data. Some methodologies used in the evaluation are questionable and may have biased its results. While this evaluation represents a significant and monumental undertaking, there are a number of issues associated with its design and execution that remain unsettled. >Ībstract: In 1998 and again in 1999, the Lincoln Laboratory of MIT conducted a comparative evaluation of intrusion detection systems (IDSs) developed under DARPA funding. The Gaussian mixture speaker model attains 96.8% identification accuracy using 5 second clean speech utterances and 80.8% accuracy using 15 second telephone speech utterances with a 49 speaker population and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task. The experiments examine algorithmic issues (initialization, variance limiting, model order selection), spectral variability robustness techniques, large population performance, and comparisons to other speaker modeling techniques (uni-modal Gaussian, VQ codebook, tied Gaussian mixture, and radial basis functions). A complete experimental evaluation of the Gaussian mixture speaker model is conducted on a 49 speaker, conversational telephone speech database. The focus of this work is on applications which require high identification rates using short utterance from unconstrained conversational speech and robustness to degradations produced by transmission over a telephone channel. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity. >Ībstract: This paper introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification. The segmental model is also used on a TIMIT vowel classification task to evaluate its modeling capability. ![]() ![]() This algorithm is evaluated on a keyword spotting task using the Road Rally Database, and performance is shown to improve significantly over that of the primary word spotter. This segment model is used to develop a secondary processing algorithm that rescores putative events hypothesized by a primary HMM word spotter to try to improve performance by discriminating true keywords from false alarms. These statistics replace the frames in the segment and become the data that are modeled by either HMMs (hidden Markov models) or mixture models. Each speech segment is represented by a set of statistics which includes a time-varying trajectory, a residual error covariance around the trajectory, and the number of frames in the segment. Abstract: The authors present a segmental speech model that explicitly models the dynamics in a variable-duration speech segment by using a time-varying trajectory model of the speech features in the segment. ![]()
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